Abstract
The problem of finding a collection of solutions to a combinatorial problem that is optimal in terms of an inter-solution objective function exists in many application settings. For example, maximizing diversity amongst a set of solutions in a product configuration setting is desirable so that a wide range of different options is offered to a customer. Given the computationally challenging nature of these multi-solution queries, existing algorithmic approaches either apply heuristics or combinatorial search, which does not scale to large solution spaces. However, in many domains compiling the original problem into a compact representation can support computationally efficient query answering. In this paper we present a new approach to find optimal collections of solutions when the problem is compiled into a multi-valued decision diagram. We demonstrate empirically that for real-world configuration problems, both exact and approximate versions of our methods are effective and are capable of significantly outperforming state-of-the-art search-based techniques.
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Hadžić, T., Holland, A., O’Sullivan, B. (2009). Reasoning about Optimal Collections of Solutions. In: Gent, I.P. (eds) Principles and Practice of Constraint Programming - CP 2009. CP 2009. Lecture Notes in Computer Science, vol 5732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04244-7_34
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DOI: https://doi.org/10.1007/978-3-642-04244-7_34
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